Evidence map›Paper›PMID 38253973›Full record

ArticleApplied health economics and health policy2024

Comparing Preferences for Disease Profiles: A Discrete Choice Experiment from a US Societal Perspective.

Karissa M Johnston, Ivana F Audhya, Jessica Dunne, David Feeny, Peter Neumann, Daniel C Malone, Shelagh M Szabo, Katherine L Gooch

Open access · hybridAbstract read
In one paragraph

Article in Applied health economics and health policy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.3field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 5 institutions in 2 countries.

Karissa M JohnstonBroadstreet HEOR, 201-343 Railway St., Vancouver, BC, V6A 1A4, Canada. kjohnston@broadstreetheor.com.ORCID 0000-0003-4570-9972
Ivana F AudhyaSarepta Therapeutics, Inc., 215 First Street, Cambridge, MA, USA.
Jessica DunneBroadstreet HEOR, 201-343 Railway St., Vancouver, BC, V6A 1A4, Canada.
David FeenyMcMaster University, Hamilton, ON, Canada.
Peter NeumannTufts Medical Center, Boston, MA, USA.
Daniel C MaloneThe University of Utah, Salt Lake City, UT, USA.
Shelagh M SzaboBroadstreet HEOR, 201-343 Railway St., Vancouver, BC, V6A 1A4, Canada.
Katherine L GoochSarepta Therapeutics, Inc., 215 First Street, Cambridge, MA, USA.
Vancouver Coastal Health · CASarepta Therapeutics (United States) · USMcMaster University · CATufts Medical Center · USUniversity of Utah · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThere is increasing interest in expanding the elements of value to be considered when making health policy decisions. To help inform value frameworks, this study quantified preferences for disease attributes in a general public sample and examined which combination of attributes (disease profiles) are considered most important for research and treatment.

methodsA discrete choice experiment (DCE) was conducted in a US general population sample, recruited through online consumer panels. Respondents were asked to select one of a set of health conditions they believed to be most important, characterized by attributes defined by a previous qualitative study: onset age; cause of disease; life expectancy; caregiver requirement; symptom burden (characterized by the Health Utilities Index with varying levels of ambulation independence, dexterity limitations, and degree of pain and discomfort); and disease prevalence. A fractional factorial DCE design was implemented using R, and 60 choice sets were generated (separated into blocks of 10 per participant). Data were analyzed using a mixed-logit regression model, and results used to assess the likelihood of preferring disease profiles. Based on individual attribute preferences, overall preferences for disease profiles, including a profile aligned with Duchenne muscular dystrophy (DMD), were compared.

resultsFifty-two percent of respondents (n = 537) were female, and 70.6% were aged 18-54 years. Attributes considered most important were those related to life expectancy (odds ratio [OR], 95% confidence interval [CI] 1.88 [1.56-2.27] for a 50% reduction in remaining life expectancy vs no impact), and symptom burden (OR [95% CI] 1.84 [1.47-2.31] for severe vs mild burden). Greater importance was also found for pediatric onset, caregiver requirement, and diseases affecting more people. As an example of disease profile preferences, a DMD-like pediatric inherited disease with 50% reduction in life expectancy, extensive caregiver requirement, severe symptom burden, and 1:5000 prevalence had 2.37-fold higher odds of being selected as important versus an equivalent disease with adult onset and no life expectancy reduction.

conclusionsOf disease attributes included in this DCE, respondents valued higher prevalence of disease, life expectancy and symptom burden as most important for prioritizing research and treatment. Based on expressed attribute preferences, a case study of an inherited pediatric disease involving substantial reductions to length and quality of life and requiring caregiver support has relatively high odds of being identified as important compared to diseases reflecting differing attribute profiles. These findings can help inform expansions of value frameworks by identifying important attributes from the societal perspective.

Indexed as

Choice BehaviorQuality of LifeAdultChildDecision MakingFemaleHumansLife ExpectancyLogistic ModelsMalePatient PreferenceSurveys and Questionnaires

Identifiers

PMID38253973
PMCPMC11021240
OpenAlexW4391115249

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.